During the diagnosis of a machine state using the data sent from a plurality of sensors mounted in and on the machine, optimal results cannot be obtained by merely entering the sensor data intact into a diagnosing program. To obtain the optimal results, it has been necessary to appropriately execute various procedural steps such as selecting appropriate sensor data to be used for the diagnosis, preprocessing the sensor data, selecting an appropriate technique for the diagnosis, and assigning appropriate parameters to be used in the diagnosing technique. Even for machines of the same model/type, criteria for judging whether the particular machine state is abnormal vary from machine to machine, depending on the manner and environment in which the machine is operated. Accordingly, there has also been a need to establish appropriate criteria. Knowledge of the analytical procedures, judgment criteria, and other factors involved in such diagnosis, is traditionally accumulated in the user who conducts the diagnosis, and the time required for the diagnosis changes significantly according to a particular magnitude of the user's knowledge. The user's knowledge has therefore been difficult to apply to construction machines that require rapid diagnosis.
In regard to these problems, Patent Document 1, for example, describes a technique that enables knowledge about past defects in a product to be readily acquired by saving the product's characteristics data and the number of defects which have occurred in the product until then, into a database and later retrieving this data from the database during the design of a new product.
In addition, Patent Document 2, for example, describes a technique for storing combinations each of a data analytical purpose and a data analytical method into a database and presenting one of the analytical methods, depending upon the analytical purpose entered from a user terminal during the data analysis.